A platform for large-scale game-playing multi-agent systems on a high performance computing infrastructure

2016 ◽  
Vol 12 (1) ◽  
pp. 35-54 ◽  
Author(s):  
Chairi Kiourt ◽  
Dimitris Kalles
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Game Playing ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Performance Computing ◽  
Computing Infrastructure

A survey on parallel and distributed multi-agent systems for high performance computing simulations

2016 ◽  
Vol 22 ◽  
pp. 27-46 ◽  
Author(s):  
Alban Rousset ◽  
Bénédicte Herrmann ◽  
Christophe Lang ◽  
Laurent Philippe
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Performance Computing

scholarly journals High-Performance Computing Framework Based on Distributed Systems for Large-Scale Neurophysiological Data

2021 ◽  
Author(s):  
Mohsen Hadianpour ◽  
Ehsan Rezayat ◽  
Mohammad-Reza Dehaqani
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Electrophysiological Recording ◽  
Neural Data ◽  
Data Framework ◽  
Neurophysiological Data ◽  
Computing Framework ◽  
Performance Computing ◽  
Neuroscience Community

Abstract Due to the significantly drastic progress and improvement in neurophysiological recording technologies, neuroscientists have faced various complexities dealing with unstructured large-scale neural data. In the neuroscience community, these complexities could create serious bottlenecks in storing, sharing, and processing neural datasets. In this article, we developed a distributed high-performance computing (HPC) framework called `Big neuronal data framework' (BNDF), to overcome these complexities. BNDF is based on open-source big data frameworks, Hadoop and Spark providing a flexible and scalable structure. We examined BNDF on three different large-scale electrophysiological recording datasets from nonhuman primate’s brains. Our results exhibited faster runtimes with scalability due to the distributed nature of BNDF. We compared BNDF results to a widely used platform like MATLAB in an equitable computational resource. Compared with other similar methods, using BNDF provides more than five times faster performance in spike sorting as a usual neuroscience application.

scholarly journals Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran
Keyword(s):  
High Performance Computing ◽  
Assignment Problem ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Metagenomic Data ◽  
Taxonomic Assignment ◽  
High Performance Computing System ◽  
Powerful Approach ◽  
Performance Computing

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.

scholarly journals Building a High Performance Computing Infrastructure for Novosibirsk Scientific Center

2011 ◽  
Vol 331 (5) ◽  
pp. 052020
Author(s):  
A Adakin ◽  
S Belov ◽  
D Chubarov ◽  
V Kalyuzhny ◽  
V Kaplin ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Scientific Center ◽  
Performance Computing ◽  
Computing Infrastructure

scholarly journals Trust in multi-agent systems

2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Comprehensive Treatment ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Core ◽  
Open Distributed Systems ◽  
Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

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